感知器 算法训练二元线性分类器。结构体数据使用感知器学习规则

找到给定的二分类标签数据超平面。

-PERCEPTRON Perceptron algorithm to train binary linear classifier.



　Synopsis:

　 model = perceptron(data)

　 model = perceptron(data,options)

　 model = perceptron(data,options,init_model)



　Descr iption:

　 model = perceptron(data) uses the Perceptron learning rule

　　to find separating hyperplane from given binary labeled data.



　 model = perceptron(data,options) specifies stopping condition of

　　the algorithm in structure options:

　　 .tmax [1x1]... maximal number of iterations.



　　If tmax==-1 then it only returns index (model.last_update)

　　of data vector which should be used by the algorithm for updating

　　the linear rule in the next iteration.



　 model = perceptron(data,options,init_model) specifies initial model

　　which must contain:

　　 .W [dim x 1] ... normal vector.

　　 .b [1x1] ... bias of hyperplane.

　　 .t [1x1] (optional) ... iteration number.



　Input:

　 data [struct] Labeled (binary) training data.

　　.X [dim x num